1. Field of the Invention
The present invention relates to an object recognition apparatus for detecting a predetermined object in an input image, a control method for the object recognition apparatus, and a storage medium.
2. Description of the Related Art
In recent years, a function for detecting a person's face in an image that is being captured and performing object tracking has rapidly become widespread in digital cameras and video cameras. Such a facial detection and tracking function is extremely useful for automatically focusing on the object to be captured and adjusting the exposure. Technology such as that proposed in Viola and Jones, “Rapid Object Detection using Boosted Cascade of Simple Features”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR2001) has been used to advance the practical use of technology for detecting a face in an image.
Meanwhile, the target to be recognized when applying such recognition technology varies depending on the user and the usage situation. Also, achieving the recognition of various objects requires the provision of dictionaries corresponding to the objects that are to be recognized. However, if there is a wide variety of recognition targets, it becomes practically impossible to collect image patterns including recognition targets and images not including recognition targets by hand. In view of this, an approach has been adopted in which object detection is performed depending on the user and usage situation by an object being designated to be the recognition target in an image and then tracking the designated object. Furthermore, in order to handle changes in the appearance of the object and drift due to the background when tracking the designated object, Grabner and Bischof, “On-line Boosting and Vision”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR2006) proposes a method for tracking while performing online learning using object patterns as positive examples and background patterns as negative examples.
However, when visual contact of the object is lost for example, tracking is paused, and it is necessary to again designate the object for attempting to resume tracking in a scene with a different background. If an attempt is made to detect the object in an image without designation, erroneous detection occurs due to a background that has not been learned yet. The cause for this is that learning was performed using a negative example biased toward the background pattern from before tracking was stopped.
In light of the above-described problem, the present invention provides technology for performing highly precise object detection using an appropriate recognition model, even in the case where tracking is paused and then resumed in a scene with a different background.